Image Segmentation
Keras
ONNX
English
tensorflow
medical-imaging
segmentation
in-context-learning
interactive-segmentation
ct
mri
Instructions to use machauer-p/lisp-net with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use machauer-p/lisp-net with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://machauer-p/lisp-net") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c1c0d7f0f8bff937812c5a8669459694f02b8cfa8d4c5cbbcc95128d6a198052
- Size of remote file:
- 113 MB
- SHA256:
- c8c85b13dfcff3726599ab896dc16c39c09791b5dc984f7afa0e52f7ff686739
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